the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Jun 2023
Evaluating F2 region long term trends using the IRI model: A feasible approximation for experimental trends?
Bruno S. Zossi
Trinidad Duran
Franco D. Medina
Blas F. de Haro Barbas
Yamila Melendi
Ana G. Elias
Abstract. The International Reference Ionosphere (IRI) is a widely used empirical model of the ionosphere based on observations from a worldwide network of ionospheric stations. Therefore, it is reasonable to expect that it captures long-term changes in key ionospheric parameters, such as foF2 and hmF2 linked to trend forcings like greenhouse gases increasing concentration and the Earth's magnetic field secular variation. Despite the numerous reported trends in foF2 and hmF2 derived from experimental data and model results, there are persistent inconsistencies that require continuous refinement of trend estimation methods and regular data updates. This ongoing effort is crucial to address the inherent challenges posed by the weak signal-to-noise ratio associated with studying long-term trends in the ionosphere. Furthermore, the experimental verification of these trends remains challenging, primarily due to time and spatial coverage limitations of measured data series. Achieving these needs for long-term trend accurate detection requires extensive global coverage and resolution of ionospheric measurements together with long enough periods spanning multiple solar cycles to properly filter out variations of shorter term than the sought trend. Considering these challenges, IRI-modeled foF2 and hmF2 parameters offer a valuable alternative for assessing trends and obtaining a first approximation of a plausible global picture representative of experimental trends. This work presents these global trend patterns considering the period 1960–2022 using the IRI-Plas 2020 version, which are consistent with other model predictions. A verification was performed for foF2 trends, considering data from 9 mid-latitude stations, and a reasonable level of agreement was observed. It is concluded that IRI model can be a valuable tool for obtaining preliminary approximations of experimental trends.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Bruno S. Zossi et al.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1335', Anonymous Referee #1, 03 Jul 2023
This is interesting paper, which deals with another possibility how to study long-term trends in foF2 (critical frequency of ionospheric F2 layer, which corresponds to maximum electron density in the ionosphere). The main advantage of approach suggested by authors is global geographical coverage of foF2 trends compared to local or regional trends analyzed until, which were based on ground-based observations. The utilization of the global empirical model the International Reference Ionosphere (IRI) allows for global coverage. On the other hand, the results are proxy of trends and cannot reach accuracy of local trends but the results are the first results providing some information on foF2 trends globally. I recommend publish the paper after moderate revision.
Comments:
Line 46: “geomagnetic equator” should be “geomagnetic equator in some longitudinal ranges” – to be more correct.
Lines 92-93: The 15th day of a month need not be good approximation of monthly medians. If the 15th day occurs in the maximum or minimum of the 27-day variation, then it differs significantly from monthly median. Smooth variation in IRI does not mean no variation. However, we can assume that in the case of large number of data as it is the case of long-term trend investigations the effects of maxima and minima of the 27-day variation essentially cancel out. Nevertheless the usage of monthly medians in future work would increase reliability of results.
Lines 106-108: Lastovicka and Buresova (2023) recommended F30 as the best solar proxy followed by Mg II used by authors. However, I understand that authors prepared their paper essentially before the paper by Lastovicka and Buresova has been published and Mg II appears also useful solar proxy.
Line 161: “Stronger” should be “Weaker” according to Fig. 2 – trends in February and June are only about -1%/decade.
Page 8, Table 4: Some MREs, particularly for 00 LT, are too high – e.g. for Townsville α is not small at 00 LT (the second highest), nevertheless the corresponding MREs are very high. Please make a comment on that in the paper with possible explanation.
Line 218: Delete “(highest values above the geomagnetic equator)” – this is unnecessary and incorrect statement.
Line 269: “represented by” should be “derived from” – this is more accurate.
Wording and misprints:
Line 107: “based in recent” should be “based on recent”
Line 235 and throughout the paper: “valley” – the term used usually in literature is “trough”
Lastovicka, J., Buresova, D., 2023. Relationships between foF2 and various solar activity proxies. Space Weather, 21, e2022SW003359. https://doi.org/10.1029/2022SW003359
Citation: https://doi.org/10.5194/egusphere-2023-1335-RC1 - AC1: 'Reply on RC1', Ana G. Elias, 06 Sep 2023
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CC1: 'Comment on egusphere-2023-1335', David Themens, 06 Jul 2023
Realistically, longterm trend in the IRI can only be attributed to processes that adhere to changes in the drivers of the model themselves. As the IRI does not include a greenhouse gas-related index or driver and does not include any longterm trend parameters except solar activity, it cannot represent the impacts of that in its output. There is no multi-year term in the IRI parameterization, except solar activity, that would allow it to represent such trends even if they existed in the data used to fit the model. The IRI is just an interpolation between a low solar activity and a high solar activity map of foF2 and M3000F2, it doesn't care about the year or date outside of that.It can, however, represent changes resulting from long term processes like the shifting of the geomagnetic field, since the IRI uses a modip or geomagnetic coordinate system (depending on the sub-model) and the magnetic field model has been updated over time. In fact, you could try to use the IRI to control against the impacts of geomagnetic field migration in search of climate change impacts, but the model output itself explicitely does not include lower atmospheric climate forcing. The impacts shown in your figures is likely entirely just the impact of the shifting magnetic field and the statistically weak solar activity over the last two cycles. If you ran the model and forced the solar activity term to be constant, you would not see anything other than the geomagnetic field migration impact. Given that you try to remove the MgII forcing later on anyway, there seems to be no reason why you shouldn't just force it to a constant to verify your hypothesis anyway.Citation: https://doi.org/
10.5194/egusphere-2023-1335-CC1 - AC3: 'Reply on CC1', Ana G. Elias, 06 Sep 2023
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RC2: 'Comment on egusphere-2023-1335', Anonymous Referee #2, 02 Aug 2023
This paper explores the use of the semi-empirical IRI model for investigating long-term (multi-decadal) trends in the ionosophere - specifically the height and critical frequency (proportional to the electron concentration) of the main F2 layer. The authors show that there is reasonably good agreement between long-term changes predicted by IRI data with the solar cycle variation removed, and a number of ionosonde stations at different geomagnetic latitudes. There is also fair agreement with the WACCM-X high top chemistry-climate model (used in two previously published studies). The conclusion is that the IRI model, which is straightforward to run on a single processor, is a useful substitute for a whole atmosphere chemistry-climate model.
The major issue that was not clear to me is the role of greenhouse gases in these trends (also raised in the comment by David Themens). The authors state at line 247 "The overall negative trends in both, foF2 and hmF2, is in agreement with that expected from increasing greenhouse concentration. Taking into account that IRI model does not include any forcing linked to these gases, the trends observed can be attributed to the data." What does this second sentence mean? What is the "data" being referred to? If the IRI model is periodically fitted to ionosonde observations, which are affected by greenhouse gas-induced changes, then it must already implicitly incorporate the effect of greenhouse gases. Although you state at line 80: "According to IRI general specifications, we expect it to somehow force variations linked to changes in the geomagnetic field, since it uses the IGRF model to specify geomagnetic poles and equator, but not those variations expected from the increasing greenhouse gases concentration." This is all very unclear.
Since the IRI model is fitted to ionosonde data, it is surely to be expected that there will be good agreement with the ionosonde data shown in Figs. 3 and 4. It seems rather circular, so I don't understand what the comparison really tests. It would be very helpful to provide a deeper description for the reader of exactly how the IRI model is fitted to ionosonde data e.g. how often the fitting takes place, over how many stations, are satellite measurements also used?
Minor points and corrections
line 75: "used to fix solar..."
line 78: "we decided to ..."
line 90: define CCIR maps
line 162: Figure 2 does not contain upper and lower panels
line 169: "generally good agreement"
line 205: "in the NmF2 trend case..."
line 214: "hmF2, the Cnossen (2020)..."
line 241: "...the Cnossen (2020) negative band"
line 254: "...to the hmF2 case."
Citation: https://doi.org/10.5194/egusphere-2023-1335-RC2 - AC2: 'Reply on RC2', Ana G. Elias, 06 Sep 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1335', Anonymous Referee #1, 03 Jul 2023
This is interesting paper, which deals with another possibility how to study long-term trends in foF2 (critical frequency of ionospheric F2 layer, which corresponds to maximum electron density in the ionosphere). The main advantage of approach suggested by authors is global geographical coverage of foF2 trends compared to local or regional trends analyzed until, which were based on ground-based observations. The utilization of the global empirical model the International Reference Ionosphere (IRI) allows for global coverage. On the other hand, the results are proxy of trends and cannot reach accuracy of local trends but the results are the first results providing some information on foF2 trends globally. I recommend publish the paper after moderate revision.
Comments:
Line 46: “geomagnetic equator” should be “geomagnetic equator in some longitudinal ranges” – to be more correct.
Lines 92-93: The 15th day of a month need not be good approximation of monthly medians. If the 15th day occurs in the maximum or minimum of the 27-day variation, then it differs significantly from monthly median. Smooth variation in IRI does not mean no variation. However, we can assume that in the case of large number of data as it is the case of long-term trend investigations the effects of maxima and minima of the 27-day variation essentially cancel out. Nevertheless the usage of monthly medians in future work would increase reliability of results.
Lines 106-108: Lastovicka and Buresova (2023) recommended F30 as the best solar proxy followed by Mg II used by authors. However, I understand that authors prepared their paper essentially before the paper by Lastovicka and Buresova has been published and Mg II appears also useful solar proxy.
Line 161: “Stronger” should be “Weaker” according to Fig. 2 – trends in February and June are only about -1%/decade.
Page 8, Table 4: Some MREs, particularly for 00 LT, are too high – e.g. for Townsville α is not small at 00 LT (the second highest), nevertheless the corresponding MREs are very high. Please make a comment on that in the paper with possible explanation.
Line 218: Delete “(highest values above the geomagnetic equator)” – this is unnecessary and incorrect statement.
Line 269: “represented by” should be “derived from” – this is more accurate.
Wording and misprints:
Line 107: “based in recent” should be “based on recent”
Line 235 and throughout the paper: “valley” – the term used usually in literature is “trough”
Lastovicka, J., Buresova, D., 2023. Relationships between foF2 and various solar activity proxies. Space Weather, 21, e2022SW003359. https://doi.org/10.1029/2022SW003359
Citation: https://doi.org/10.5194/egusphere-2023-1335-RC1 - AC1: 'Reply on RC1', Ana G. Elias, 06 Sep 2023
-
CC1: 'Comment on egusphere-2023-1335', David Themens, 06 Jul 2023
Realistically, longterm trend in the IRI can only be attributed to processes that adhere to changes in the drivers of the model themselves. As the IRI does not include a greenhouse gas-related index or driver and does not include any longterm trend parameters except solar activity, it cannot represent the impacts of that in its output. There is no multi-year term in the IRI parameterization, except solar activity, that would allow it to represent such trends even if they existed in the data used to fit the model. The IRI is just an interpolation between a low solar activity and a high solar activity map of foF2 and M3000F2, it doesn't care about the year or date outside of that.It can, however, represent changes resulting from long term processes like the shifting of the geomagnetic field, since the IRI uses a modip or geomagnetic coordinate system (depending on the sub-model) and the magnetic field model has been updated over time. In fact, you could try to use the IRI to control against the impacts of geomagnetic field migration in search of climate change impacts, but the model output itself explicitely does not include lower atmospheric climate forcing. The impacts shown in your figures is likely entirely just the impact of the shifting magnetic field and the statistically weak solar activity over the last two cycles. If you ran the model and forced the solar activity term to be constant, you would not see anything other than the geomagnetic field migration impact. Given that you try to remove the MgII forcing later on anyway, there seems to be no reason why you shouldn't just force it to a constant to verify your hypothesis anyway.Citation: https://doi.org/
10.5194/egusphere-2023-1335-CC1 - AC3: 'Reply on CC1', Ana G. Elias, 06 Sep 2023
-
RC2: 'Comment on egusphere-2023-1335', Anonymous Referee #2, 02 Aug 2023
This paper explores the use of the semi-empirical IRI model for investigating long-term (multi-decadal) trends in the ionosophere - specifically the height and critical frequency (proportional to the electron concentration) of the main F2 layer. The authors show that there is reasonably good agreement between long-term changes predicted by IRI data with the solar cycle variation removed, and a number of ionosonde stations at different geomagnetic latitudes. There is also fair agreement with the WACCM-X high top chemistry-climate model (used in two previously published studies). The conclusion is that the IRI model, which is straightforward to run on a single processor, is a useful substitute for a whole atmosphere chemistry-climate model.
The major issue that was not clear to me is the role of greenhouse gases in these trends (also raised in the comment by David Themens). The authors state at line 247 "The overall negative trends in both, foF2 and hmF2, is in agreement with that expected from increasing greenhouse concentration. Taking into account that IRI model does not include any forcing linked to these gases, the trends observed can be attributed to the data." What does this second sentence mean? What is the "data" being referred to? If the IRI model is periodically fitted to ionosonde observations, which are affected by greenhouse gas-induced changes, then it must already implicitly incorporate the effect of greenhouse gases. Although you state at line 80: "According to IRI general specifications, we expect it to somehow force variations linked to changes in the geomagnetic field, since it uses the IGRF model to specify geomagnetic poles and equator, but not those variations expected from the increasing greenhouse gases concentration." This is all very unclear.
Since the IRI model is fitted to ionosonde data, it is surely to be expected that there will be good agreement with the ionosonde data shown in Figs. 3 and 4. It seems rather circular, so I don't understand what the comparison really tests. It would be very helpful to provide a deeper description for the reader of exactly how the IRI model is fitted to ionosonde data e.g. how often the fitting takes place, over how many stations, are satellite measurements also used?
Minor points and corrections
line 75: "used to fix solar..."
line 78: "we decided to ..."
line 90: define CCIR maps
line 162: Figure 2 does not contain upper and lower panels
line 169: "generally good agreement"
line 205: "in the NmF2 trend case..."
line 214: "hmF2, the Cnossen (2020)..."
line 241: "...the Cnossen (2020) negative band"
line 254: "...to the hmF2 case."
Citation: https://doi.org/10.5194/egusphere-2023-1335-RC2 - AC2: 'Reply on RC2', Ana G. Elias, 06 Sep 2023
Peer review completion
Journal article(s) based on this preprint
Bruno S. Zossi et al.
Bruno S. Zossi et al.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1081 KB) - Metadata XML